IDEAS home Printed from https://ideas.repec.org/p/sfu/sfudps/dp20-08.html
   My bibliography  Save this paper

Peers as treatments

Author

Listed:

Abstract

Models of social interactions are often estimated under the strong assumption that an individual's choices are a direct function of the average observed characteristics of his or her reference group. This paper interprets social interactions in a less restrictive potential outcomes framework in which interaction with a given peer or peer group is considered a treatment with an unknown treatment effect. In this framework, conventional peer effect regressions can be interpreted as characterizing treatment effect heterogeneity. This framework is then used to clarify identification and interpretation of commonly-used peer effect models and to suggest avenues for improving upon them.

Suggested Citation

  • Brian Krauth, 2020. "Peers as treatments," Discussion Papers dp20-08, Department of Economics, Simon Fraser University.
    • Handle: RePEc:sfu:sfudps:dp20-08
    as

    Download full text from publisher

    File URL: http://www.sfu.ca/repec-econ/sfu/sfudps/dp20-08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bryan S. Graham, 2008. "Identifying Social Interactions Through Conditional Variance Restrictions," Econometrica, Econometric Society, vol. 76(3), pages 643-660, May.
    2. Mary A. Burke & Tim R. Sass, 2013. "Classroom Peer Effects and Student Achievement," Journal of Labor Economics, University of Chicago Press, vol. 31(1), pages 51-82.
    3. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    4. Victor Lavy & Analia Schlosser, 2011. "Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 1-33, April.
    5. Eisenkopf, Gerald & Hessami, Zohal & Fischbacher, Urs & Ursprung, Heinrich W., 2015. "Academic performance and single-sex schooling: Evidence from a natural experiment in Switzerland," Journal of Economic Behavior & Organization, Elsevier, vol. 115(C), pages 123-143.
    6. Bhattacharya, Debopam, 2009. "Inferring Optimal Peer Assignment From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 486-500.
    7. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    8. Peter Arcidiacono & Gigi Foster & Natalie Goodpaster & Josh Kinsler, 2012. "Estimating spillovers using panel data, with an application to the classroom," Quantitative Economics, Econometric Society, vol. 3(3), pages 421-470, November.
    9. Scott E. Carrell & Bruce I. Sacerdote & James E. West, 2013. "From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation," Econometrica, Econometric Society, vol. 81(3), pages 855-882, May.
    10. Alejandro Gaviria & Steven Raphael, 2001. "School-Based Peer Effects And Juvenile Behavior," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 257-268, May.
    11. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    12. Caroline Hoxby, 2000. "Peer Effects in the Classroom: Learning from Gender and Race Variation," NBER Working Papers 7867, National Bureau of Economic Research, Inc.
    13. Victor Lavy & Analía Schlosser, 2011. "Corrigendum: Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 268-268, July.
    14. Bryan S. Graham & Guido W. Imbens & Geert Ridder, 2010. "Measuring the Effects of Segregation in the Presence of Social Spillovers: A Nonparametric Approach," NBER Working Papers 16499, National Bureau of Economic Research, Inc.
    15. Jane Cooley Fruehwirth, 2014. "Can Achievement Peer Effect Estimates Inform Policy? A View from Inside the Black Box," The Review of Economics and Statistics, MIT Press, vol. 96(3), pages 514-523, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Haining & Cheng, Zhiming & Smyth, Russell, 2018. "Do migrant students affect local students’ academic achievements in urban China?," Economics of Education Review, Elsevier, vol. 63(C), pages 64-77.
    2. Wennberg, Karl & Norgren, Axel, 2021. "Models of Peer Effects in Education," Working Papers 21/3, Stockholm School of Economics, Center for Educational Leadership and Excellence.
    3. Braun, Martin & Verdier, Valentin, 2023. "Estimation of spillover effects with matched data or longitudinal network data," Journal of Econometrics, Elsevier, vol. 233(2), pages 689-714.
    4. Alexandra de Gendre & Nicolás Salamanca, 2020. "On the Mechanisms of Ability Peer Effects," Melbourne Institute Working Paper Series wp2020n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Angela Granger-Serrano & Alexander Villarraga-Orjuela, 2021. "Peer Effects on First-Year University Students’ Results: The Role of Classmates’ Academic Performance and Socioeconomic Status," Mathematics, MDPI, vol. 9(23), pages 1-26, December.
    6. Michela Maria Tincani, 2017. "Heterogeneous Peer Effects and Rank Concerns: Theory and Evidence," CESifo Working Paper Series 6331, CESifo.
    7. Ozkan Eren, 2017. "Differential Peer Effects, Student Achievement, and Student Absenteeism: Evidence From a Large-Scale Randomized Experiment," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 745-773, April.
    8. Humlum, Maria Knoth & Thorsager, Mette, 2021. "The Importance of Peer Quality for Completion of Higher Education," Economics of Education Review, Elsevier, vol. 83(C).
    9. Michela Tincani, 2017. "Heterogeneous Peer Effects and Rank Concerns: Theory and Evidence," Working Papers 2017-006, Human Capital and Economic Opportunity Working Group.
    10. Feng, Han & Li, Jiayao, 2016. "Head teachers, peer effects, and student achievement," China Economic Review, Elsevier, vol. 41(C), pages 268-283.
    11. Feng, Shuaizhang & Kim, Jun Hyung & Yang, Zhe, 2021. "Effects of Childhood Peers on Personality Skills," IZA Discussion Papers 14952, Institute of Labor Economics (IZA).
    12. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    13. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    14. Simone Balestra & Aurélien Sallin & Stefan C. Wolter, 2023. "High-Ability Influencers? The Heterogeneous Effects of Gifted Classmates," Journal of Human Resources, University of Wisconsin Press, vol. 58(2), pages 633-665.
    15. Vincent Boucher & Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2014. "Do Peers Affect Student Achievement? Evidence From Canada Using Group Size Variation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 91-109, January.
    16. Pan, Zheng & Lien, Donald & Wang, Hao, 2022. "Peer effects and shadow education," Economic Modelling, Elsevier, vol. 111(C).
    17. Chih‐Sheng Hsieh & Hans van Kippersluis, 2018. "Smoking initiation: Peers and personality," Quantitative Economics, Econometric Society, vol. 9(2), pages 825-863, July.
    18. Auestad, May Linn, 2018. "The effect of low-achieving peers," Labour Economics, Elsevier, vol. 55(C), pages 178-214.
    19. Gioia De Melo, 2011. "Peer effects identified through social networks. Evidence from Uruguayan schools," Department of Economics University of Siena 627, Department of Economics, University of Siena.
    20. Santiago Pereda-Fernández, 2017. "Social Spillovers in the Classroom: Identification, Estimation and Policy Analysis," Economica, London School of Economics and Political Science, vol. 84(336), pages 712-747, October.

    More about this item

    Keywords

    peer effect; social interactions; peer effect regressions;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sfu:sfudps:dp20-08. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Working Paper Coordinator (email available below). General contact details of provider: https://edirc.repec.org/data/desfuca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.